iSummary: Workload-based Selective Summaries for Knowledge Graph Exploration

The rapid growth in size and complexity of Knowledge Graphs available on the web has created a pressing need for efficient and effective methods to facilitate their understanding and exploration. Recently, semantic summaries have emerged as a means to quickly comprehend and explore them. However, most existing approaches are static, failing to adapt to user needs and preferences, and often struggle to scale. In this paper, we introduce iSummary, a novel and scalable approach for constructing summaries given specific user requests in terms of nodes for the summary to be based on. Given that the size and complexity of Knowledge Graphs pose challenges to efficient summary construction, our approach leverages query logs. The core idea is to harness the knowledge embedded in existing user queries to identify the most relevant resources and establish meaningful connections, thereby generating high-quality summaries. We propose an algorithm with theoretical guarantees on summary quality, operating linearly with respect to the number of queries in the log. To assess our method, we conduct experiments using two real-world datasets and multiple baselines, demonstrating that iSummary consistently outperforms existing techniques in both quality and efficiency.